ISA 401: Business Intelligence & Data Visualization

class: center, middle, inverse, title-slide

.title[
# ISA 401: Business Intelligence & Data Visualization
]
.subtitle[
## 02: A Quick Introduction to <svg viewBox="0 0 581 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"></path></svg>, <svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"></path></svg>, and LLMs
]
.author[
### <br>Fadel M. Megahed, PhD <br><br> Endres Associate Professor <br> Farmer School of Business<br> Miami University<br><br> <a href="https://twitter.com/FadelMegahed"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> <span class="citation">@FadelMegahed</span></a> <br> <a href="https://github.com/fmegahed/"><svg viewBox="0 0 496 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"></path></svg> fmegahed</a> <br> <a href="mailto:fmegahed@miamioh.edu"><svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"></path></svg> fmegahed@miamioh.edu</a><br> <a href="https://calendly.com/fmegahed"><svg viewBox="0 0 384 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:white;" xmlns="http://www.w3.org/2000/svg"> <path d="M202.021 0C122.202 0 70.503 32.703 29.914 91.026c-7.363 10.58-5.093 25.086 5.178 32.874l43.138 32.709c10.373 7.865 25.132 6.026 33.253-4.148 25.049-31.381 43.63-49.449 82.757-49.449 30.764 0 68.816 19.799 68.816 49.631 0 22.552-18.617 34.134-48.993 51.164-35.423 19.86-82.299 44.576-82.299 106.405V320c0 13.255 10.745 24 24 24h72.471c13.255 0 24-10.745 24-24v-5.773c0-42.86 125.268-44.645 125.268-160.627C377.504 66.256 286.902 0 202.021 0zM192 373.459c-38.196 0-69.271 31.075-69.271 69.271 0 38.195 31.075 69.27 69.271 69.27s69.271-31.075 69.271-69.271-31.075-69.27-69.271-69.27z"></path></svg> Automated Scheduler for Office Hours</a><br><br>
]
.date[
### Fall 2023
]

---

# Quick Refresher from Last Class

.footnote[
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**Note:** Image generated using <https://ideogram.ai/>. **Pros:** Currently best text-2-image generator while embedding text, and free  (useful for professional logos). **Cons:** Not the best with hands.
]

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# Quick Refresher from Last Class

✅ Describe course objectives & structure

✅ Define data visualization & describe its main goals

✅ Describe the BI methodology and major concepts

---

# Learning Objectives for Today's Class

- Describe how and why we use scripted languages in this course.

- Utilize the project workflow in RStudio (we will try to use that as an IDE for <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>).

- Understand the syntax, data structures and functions in both <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>.

- Understand the potential impact of LLMs on businesses and explore how they can be leveraged in the context of this class.

---
class: inverse, center, middle

# Scripted Languages (i.e., <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:white;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>)

---

# Pedagogy Behind Using Scripted Languages

.center[### <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>]

```r
crashes = 
  # reading the data directly from the source
  readr::read_csv("https://data.cincinnati-oh.gov/api/views/rvmt-pkmq/rows.csv?accessType=DOWNLOAD") |> 
  
  # changing all variable names to snake_case
  janitor::clean_names() |> 
  
  # selecting variables of interest
  dplyr::select(address_x, latitude_x, longitude_x, cpd_neighborhood, datecrashreported, instanceid, typeofperson, weather) |>
  
  # engineering some features from the data
  dplyr::mutate(
    datetime = lubridate::parse_date_time(datecrashreported, orders = "'%m/%d/%Y %I:%M:%S %p", tz =  'America/New_York'),
    hour = lubridate::hour(datetime),
    date = lubridate::as_date(datetime)   
    )
```

---

# Pedagogy Behind Using Scripted Languages

.center[### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>]

```python
import pandas as pd
import datetime as dt

crashes = (
    # Reading the CSV file from a URL
    pd.read_csv('https://data.cincinnati-oh.gov/api/views/rvmt-pkmq/rows.csv?accessType=DOWNLOAD')
    
    # Renaming all columns to be lowercase and replacing spaces with underscores
    .rename(columns={col: col.lower().replace(" ", "_") for col in pd.read_csv('https://data.cincinnati-oh.gov/api/views/rvmt-pkmq/rows.csv?accessType=DOWNLOAD').columns})
    
    # Selecting only the columns of interest
    .loc[:, ["address_x", "latitude_x", "longitude_x", "cpd_neighborhood", "datecrashreported", "instanceid", "typeofperson", "weather"]]
    
    # Adding new columns: 'datetime', 'hour', and 'date' derived from 'datecrashreported'
    .assign(
        datetime=lambda df: pd.to_datetime(df['datecrashreported'], format="%m/%d/%Y %I:%M:%S %p"),  # Convert 'datecrashreported' to datetime format
        hour=lambda df: df['datetime'].dt.hour,  # Extracting the hour from 'datetime'
        date=lambda df: df['datetime'].dt.date  # Extracting the date from 'datetime'
    )
)
```

???

Here's what each major function does:

1. pd.read_csv(...): Reads the CSV file from the given URL and converts it into a Pandas DataFrame.

2. .rename(...): Renames all columns in the DataFrame according to the dictionary provided. In this case, it makes all column names lowercase and replaces spaces with underscores.

3. .loc[..., [...]]: Selects only the columns specified in the list, effectively filtering out the rest.

4. .assign(...): Adds new columns to the DataFrame based on some operation or transformation. In this case, it adds the datetime, hour, and date columns.

---

# The Beauty of Programming Languages

- Programming languages are **languages**.

- **It's just text** -- which gives you access to **two extremely powerful techniques**!!!  
  + .font100[`Ctrl` + `C` <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M384 336H192c-8.8 0-16-7.2-16-16V64c0-8.8 7.2-16 16-16l140.1 0L400 115.9V320c0 8.8-7.2 16-16 16zM192 384H384c35.3 0 64-28.7 64-64V115.9c0-12.7-5.1-24.9-14.1-33.9L366.1 14.1c-9-9-21.2-14.1-33.9-14.1H192c-35.3 0-64 28.7-64 64V320c0 35.3 28.7 64 64 64zM64 128c-35.3 0-64 28.7-64 64V448c0 35.3 28.7 64 64 64H256c35.3 0 64-28.7 64-64V416H272v32c0 8.8-7.2 16-16 16H64c-8.8 0-16-7.2-16-16V192c0-8.8 7.2-16 16-16H96V128H64z"/></svg>] 
  + .font100[`Ctrl` + `V` <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M80 96v16c0 17.7 14.3 32 32 32h60.8c16.6-28.7 47.6-48 83.2-48h62c-7.1-27.6-32.2-48-62-48H215.4C211.6 20.9 188.2 0 160 0s-51.6 20.9-55.4 48H64C28.7 48 0 76.7 0 112V384c0 35.3 28.7 64 64 64h96V400H64c-8.8 0-16-7.2-16-16V112c0-8.8 7.2-16 16-16H80zm64-40a16 16 0 1 1 32 0 16 16 0 1 1 -32 0zM256 464c-8.8 0-16-7.2-16-16V192c0-8.8 7.2-16 16-16H384v48c0 17.7 14.3 32 32 32h48V448c0 8.8-7.2 16-16 16H256zm192 48c35.3 0 64-28.7 64-64V227.9c0-12.7-5.1-24.9-14.1-33.9l-51.9-51.9c-9-9-21.2-14.1-33.9-14.1H256c-35.3 0-64 28.7-64 64V448c0 35.3 28.7 64 64 64H448z"/></svg>]

- In addition, programming languages are generally:

+ Readable (IMO way easier than trying to figure what someone did in an <svg aria-hidden="true" role="img" viewBox="0 0 384 512" style="height:1em;width:0.75em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:green;overflow:visible;position:relative;"><path d="M48 448V64c0-8.8 7.2-16 16-16H224v80c0 17.7 14.3 32 32 32h80V448c0 8.8-7.2 16-16 16H64c-8.8 0-16-7.2-16-16zM64 0C28.7 0 0 28.7 0 64V448c0 35.3 28.7 64 64 64H320c35.3 0 64-28.7 64-64V154.5c0-17-6.7-33.3-18.7-45.3L274.7 18.7C262.7 6.7 246.5 0 229.5 0H64zm90.9 233.3c-8.1-10.5-23.2-12.3-33.7-4.2s-12.3 23.2-4.2 33.7L161.6 320l-44.5 57.3c-8.1 10.5-6.3 25.5 4.2 33.7s25.5 6.3 33.7-4.2L192 359.1l37.1 47.6c8.1 10.5 23.2 12.3 33.7 4.2s12.3-23.2 4.2-33.7L222.4 320l44.5-57.3c8.1-10.5 6.3-25.5-4.2-33.7s-25.5-6.3-33.7 4.2L192 280.9l-37.1-47.6z"/></svg>)  
  
  + Open (so it is easier to <svg aria-hidden="true" role="img" viewBox="0 0 488 512" style="height:1em;width:0.95em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M488 261.8C488 403.3 391.1 504 248 504 110.8 504 0 393.2 0 256S110.8 8 248 8c66.8 0 123 24.5 166.3 64.9l-67.5 64.9C258.5 52.6 94.3 116.6 94.3 256c0 86.5 69.1 156.6 153.7 156.6 98.2 0 135-70.4 140.8-106.9H248v-85.3h236.1c2.3 12.7 3.9 24.9 3.9 41.4z"/></svg> or **ChatGPT** it)  
  
  + Reusable and reproducible (so you can reuse your code for similar problems and other people can get the same results as you easily)  
  
  + Diffable (version control is extremely powerful)

.footnote[
<html>
<hr>
</html>

**Source:** Content in "The Beauty of Programming Languages" is from [Hadley Wickham's You Can't Do Data Science in a GUI](https://speakerdeck.com/hadley/you-cant-do-data-science-in-a-gui?slide=14)
]

---

# How to Learn Any Programming Language

* 🗣 **Get hands dirty** ‼️

* 📖 Documentation! Documentation! Documentation!

* 🔎 (Not surprisingly) Learn to Google/ChatGPT: what that error message means (I do that a lot 😄)

.footnote[
<html>

<hr>

</html>

**Source:** Slide is based on [Kia Ora's How I Learn a Technology](https://stats220.earo.me/01-intro.html#7).
]

---

class: inverse center middle

# The RStudio Interface, Setup and a Project-Oriented Workflow for your Analysis

---

## RStudio Interface

.center[<img src="../../figures/rstudio-interface.png" width="80%">]

.footnote[
<html>
<hr>
</html>
image credit: Stuart Lee]

???

live

---

## Setting up RStudio (do this once for <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>)

.pull-left[
Go to **Tools** > **Global Options**:
.center[<img src="../../figures/rstudio-setup.PNG" width="100%">]
]
.pull-right[
<br>
<br>
<br>
<br>
Uncheck `Workspace` and `History`, which helps to keep <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> working environment fresh and clean every time you switch between projects.
]

---

## What is a project?

* Each university course is a project, and get your work organised.
* A self-contained project is a folder that contains all relevant files, for example my `ISA 401/` 📂 includes:
   + `isa401.Rproj`
   + `lectures/`  
     +  `01_Introduction/`  
          *  `01-Introduction.Rmd`, etc.   
     +  `02_llms_r_python/`  
          *  `02_llms_r_python.Rmd`, etc. 
* All working files are **relative** to the **project root** (i.e. `isa401/`).
* The project should just work on a different computer (in most cases).

---

## Setting up RStudio (do this once for <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>)

.pull-left-3[

.font70[

.center[**Installing Python**]

1. (Preferred) Install <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> via `reticulate::install_miniconda()`.

2. Install <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>  from [python.org](https://www.python.org/downloads/).

3. Setup virtual environment and install needed <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:gold;overflow:visible;position:relative;"><path d="M50.7 58.5L0 160H208V32H93.7C75.5 32 58.9 42.3 50.7 58.5zM240 160H448L397.3 58.5C389.1 42.3 372.5 32 354.3 32H240V160zm208 32H0V416c0 35.3 28.7 64 64 64H384c35.3 0 64-28.7 64-64V192z"/></svg>

.center[**Helping R Find the Correct Version of Python**]

1. Edit your RProfile for the project to connect to a specific version of Python,
.font70[`Sys.setenv(RETICULATE_PYTHON = "C:\\tools\\Anaconda3\\envs\\spc_gpt\\python.exe")`]

2. Configure a default version of Python to be used with RStudio via Tools -> Global Options... -> Python
]
]

.pull-right-3[
<iframe src="python_with_r_studio.html" width="100%" height="470px" data-external="1"></iframe>
]

.footnote[
<html>
<hr>
</html>

**Note:** Feel free to use other IDEs of your choice for Python and/or R. During class, I will use RStudio for both to reduce the possible friction from using and/or setting up multiple IDEs.
]

---
class: inverse, center, middle

# Operators 101

---

# Assignment

.pull-left[

### <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

```r
x1 <- 5
x2 = 5
5 -> x3

print(paste0("The values of x1, x2, and x3 are ", x1, ", ", x2, ", and ", x3, " respectively"))
```

```
## [1] "The values of x1, x2, and x3 are 5, 5, and 5 respectively"
```

]

.pull-right[
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>

```python
x1 = 5 # no '<-' operator
x2 = 5 # only "="
x3 = 5  # no '->' operator

print(f"The values of x1, x2, and x3 are {x1}, {x2}, and {x3} respectively")
```

```
## The values of x1, x2, and x3 are 5, 5, and 5 respectively
```
]

The assignment consists of three parts:

- The left-hand side: **variable names** (`x1` or `x2`), 
  - The assignment operator: `=`, and the right-hand side: **values** (`5`)

---

# Retrieval

We can retrieve/call the object using its name as follows:

```r
x1
```

```
## [1] 5
```

```r
x3
```

```
## [1] 5
```

---

# Retrieval: Three Common Errors

**Case issue:** object names in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> and <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> are **case sensitive**.

```r
X1 # should be x1 instead of X1 (see last slide)
```

```
## Error in eval(expr, envir, enclos): object 'X1' not found
```

**Typo:** A spelling error of some sort (with a corresponding <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> error message)

```python
y3 # should be x3 instead of y3 (see last slide)
```

```
## name 'y3' is not defined
```

**Object not saved:** e.g., you clicked **Enter** instead of **Ctrl + Enter** when running your code

```r
rm(x2) # removing x2 from the global environment to mimic error
x2 # x2 is not in the global environment (see environment)
```

```
## Error in eval(expr, envir, enclos): object 'x2' not found
```

---

# Arthimetic Operators

While we will not specifically talk about doing math in this course, the operators below are good to know.

<table border="1">
  <tr>
    <th>R</th>
    <th>Description</th>
    <th>Python</th>
  </tr>
  <tr>
    <td>+</td>
    <td>addition</td>
    <td>+</td>
  </tr>
  <tr>
    <td>-</td>
    <td>subtraction</td>
    <td>-</td>
  </tr>
  <tr>
    <td>*</td>
    <td>multiplication</td>
    <td>*</td>
  </tr>
  <tr>
    <td>/</td>
    <td>division</td>
    <td>/</td>
  </tr>
  <tr>
    <td>^ or **</td>
    <td>exponentiation</td>
    <td>**</td>
  </tr>
  <tr>
    <td>x %% y</td>
    <td>modulus (x mod y)</td>
    <td>x % y</td>
  </tr>
  <tr>
    <td>x %/% y</td>
    <td>integer division</td>
    <td>x // y</td>
  </tr>
</table>

</center>
</html>

---

# Logical Operators

Logical operators are operators that return `TRUE` (`True` <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>) and `FALSE` (`False` in <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>) values.

<table border="1">
  <tr>
    <th>R</th>
    <th>Description</th>
    <th>Python</th>
  </tr>
  <tr>
    <td>&lt;</td>
    <td>less than</td>
    <td>&lt;</td>
  </tr>
  <tr>
    <td>&lt;=</td>
    <td>less than or equal to</td>
    <td>&lt;=</td>
  </tr>
  <tr>
    <td>&gt;</td>
    <td>greater than</td>
    <td>&gt;</td>
  </tr>
  <tr>
    <td>&gt;=</td>
    <td>greater than or equal to</td>
    <td>&gt;=</td>
  </tr>
  <tr>
    <td>==</td>
    <td>exactly equal to</td>
    <td>==</td>
  </tr>
  <tr>
    <td>!=</td>
    <td>not equal to</td>
    <td>!=</td>
  </tr>
  <tr>
    <td>!x</td>
    <td>Not x</td>
    <td>not x</td>
  </tr>
  <tr>
    <td>x &amp; y</td>
    <td>x AND y</td>
    <td>x &amp; y</td>
  </tr>
  <tr>
    <td>isTRUE(x)</td>
    <td>test if X is TRUE</td>
    <td>x is `True`</td>
  </tr>
</table>

---
class: inverse, center, middle

# 101: Syntax, Data Types, Data Structures and Functions

---

# Coding Style

> .font150[Good coding style is like correct punctuation: you can manage without it, butitsuremakesthingseasiertoread.
 <br> -- [The tidyverse style guide](https://style.tidyverse.org)]

.pull-left[
### <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> style guide

✅ `snake_case`

]
.pull-right[
### <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> style guide

✅ `PascalCase` (Python)
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#34)
]

---

# <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> is a Vector Language: Types and Attributes

.pull-left[
- **Vectors** come in **two flavors**, which differ by their **elements' types:** 
    * **atomic vectors --** all elements **must have the same type** 
    * **lists --** elements **can** be different
- Vector have two important **attributes:**  
  * **Dimension** turns vectors into matrices and arrays, checked using `dim(object_name)`. 
  * The **class** attribute powers the S3 object system, checked using `class(object_name)`.
]

.pull-right[
.center[<img src="https://d33wubrfki0l68.cloudfront.net/2ff3a6cebf1bb80abb2a814ae1cfc67b12817713/ae848/diagrams/vectors/summary-tree.png" width="85%">]

```r
x_vec = rnorm(n=10, mean = 0, sd = 1)

class(x_vec)
```

```
## [1] "numeric"
```
]

.footnote[
<html>
<hr>
</html>
**Source:** The content and image are from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

.pull-left[
.center[<img src="https://d33wubrfki0l68.cloudfront.net/eb6730b841e32292d9ff36b33a590e24b6221f43/57192/diagrams/vectors/summary-tree-atomic.png" width="100%">]
]

.pull-right[

```r
dim(x_vec) 
```

```
## NULL
```

**Atomic vectors have a dim of NULL, which distinguishes it from 1D arrays 😲!!!**
]

.footnote[
<html>
<hr>
</html>
**Source:** The image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

.center[<img src="https://d33wubrfki0l68.cloudfront.net/8a3d360c80da1186b1373a0ff0ddf7803b96e20d/254c6/diagrams/vectors/atomic.png" width="60%">]

- To check the **type of** an object in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>, you can use the function `typeof`:

```r
typeof(x_vec)
```

```
## [1] "double"
```

.footnote[
<html>
<hr>
</html>
**Source:** The image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html)
]

---

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/four_popular_data_types.png" alt="The four data types that we will utilize the most in our course." width="100%" />
<p class="caption">The four data types that we will utilize the most in our course.</p>
</div>

---

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">A visual representation of different types of atomic vectors</p>
</div>

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

Each of the four primary types has a special syntax to create an individual value:

- Logicals can be written in full (`TRUE` or `FALSE`), or abbreviated (`T` or `F`).

- Doubles can be specified in decimal (`0.1234`), scientific (`1.23e4`), or hexadecimal (`0xcafe`) form.   
    * There are three special values unique to doubles: `Inf`, `-Inf`, and `NaN` (not a number).   
    * These are special values defined by the floating point standard.

- Integers are written similarly to doubles but must be followed by `L`(`1234L`, `1e4L`, or `0xcafeL`), and can not contain fractional values.

- Strings are surrounded by `"` (e.g., `"hi"`) or `'` (e.g., `'bye'`). Special characters are escaped with `\` see `?Quotes` for full details.

.footnote[
<html>
<hr>
</html>
**Source:** The content of this slide is verbatim from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#scalars)
]

---

# Translating <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Data Types to <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>

<html>
<center>
<table border="1">
  <tr>
    <th>R Data Type</th>
    <th>Description</th>
    <th>Python Equivalent</th>
  </tr>
  <tr>
    <td>numeric</td>
    <td>Decimal numbers</td>
    <td>float</td>
  </tr>
  <tr>
    <td>integer</td>
    <td>Whole numbers</td>
    <td>int</td>
  </tr>
  <tr>
    <td>character</td>
    <td>Text or strings</td>
    <td>str</td>
  </tr>
  <tr>
    <td>factor</td>
    <td>Categorical data</td>
    <td>pandas.Categorical or str</td>
  </tr>
  <tr>
    <td>Date</td>
    <td>Date values</td>
    <td>datetime.date</td>
  </tr>
  <tr>
    <td>POSIXct</td>
    <td>Date and time</td>
    <td>datetime.datetime</td>
  </tr>
    <tr>
    <td>logical</td>
    <td>Boolean (TRUE/FALSE)</td>
    <td>bool</td>
  </tr>
  <tr>
    <td>complex</td>
    <td>Complex numbers</td>
    <td>complex</td>
  </tr>
</table>
</center>
</html>

---

<div class="figure" style="text-align: center">
<img src="data:image/png;base64,#../../figures/legos-jbryan-types.png" alt="A visual representation of different types of atomic vectors" width="100%" />
<p class="caption">Keeping the  visual representation of different types of atomic vectors in your head!!</p>
</div>

```r
dept = c('ACC', 'ECO', 'FIN', 'ISA', 'MGMT')
nfaculty = c(18L, 19L, 14L, 25L, 22L)
```

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)
]

---

.center[<img src="../../figures/legos-jbryan-structures.png" width="92%">]

.footnote[
<html>
<hr>
</html>
**Source:** The images are from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats#readme)  
]

---

```r
library(tibble)

fsb_tbl <- tibble(
  department = dept, 
  count = nfaculty, 
  percentage = count / sum(count))
fsb_tbl
```

```
## # A tibble: 5 × 3
##   department count percentage
##   <chr>      <int>      <dbl>
## 1 ACC           18      0.184
## 2 ECO           19      0.194
## 3 FIN           14      0.143
## 4 ISA           25      0.255
## 5 MGMT          22      0.224
```

---

An object contains elements of **different data types**.

.center[<img src="../../figures/legos-jbryan-list.png" width="25%">]

.footnote[
<html>
<hr>
</html>
**Source:** The image is adapted from the excellent [lego-rstats GitHub Repository by Jenny Bryan](https://github.com/jennybc/lego-rstats/blob/master/lego-rstats_014.jpg)
]

---

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9628eed602df6fd55d9bced4fba0a5a85d93db8a/36c16/diagrams/vectors/list.png" width="100%">]

```r
lst <- list( # list constructor/creator
* 1:3, # atomic double/numeric vector  of length = 3
* "a", # atomic character vector of length = 1 (aka scalar)
* c(TRUE, FALSE, TRUE), # atomic logical vector of length = 3
* c(2.3, 5.9) # atomic double/numeric vector of length =3
)
lst # printing the list
```

```
## [1] "1:3"                  "a"                    "c(TRUE, FALSE, TRUE)"
## [4] "c(2.3, 5.9)"
```

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#lists)
]

---

.pull-left[
### data type

```r
typeof(lst) # primitive type
```

```
## [1] "list"
```
### data class

```r
class(lst) # type + attributes
```

```
## [1] "list"
```
]
.pull-right[
### data structure

```r
str(lst)
# sublists can be of diff lengths and types
```

```
## List of 4
##  $ : int [1:3] 1 2 3
##  $ : chr "a"
##  $ : logi [1:3] TRUE FALSE TRUE
##  $ : num [1:2] 2.3 5.9
```
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/02-import-export.html#6).
]

---

A list can contain other lists, i.e. **recursive**

```r
# a named list
str(
* list(first_el = lst, second_el = iris)
  )
```

```
## List of 2
##  $ first_el :List of 4
##   ..$ : int [1:3] 1 2 3
##   ..$ : chr "a"
##   ..$ : logi [1:3] TRUE FALSE TRUE
##   ..$ : num [1:2] 2.3 5.9
##  $ second_el:'data.frame':	150 obs. of  5 variables:
##   ..$ Sepal.Length: num [1:150] 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##   ..$ Sepal.Width : num [1:150] 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##   ..$ Petal.Length: num [1:150] 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##   ..$ Petal.Width : num [1:150] 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##   ..$ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
```

---

.pull-left[
Subset by `[]`

```r
lst[1]
```

```
## [[1]]
## [1] 1 2 3
```
]
.pull-right[
Subset by `[[]]`

```r
lst[[1]]
```

```
## [1] 1 2 3
```
]

.center[<img src="../../figures/pepper.png" width="50%">]

.footnote[
<html>
<hr>
</html>
**Sources:** The slide is based on [Earo Wang's STAT 220 slides](https://stats220.earo.me/02-import-export.html#10) and image is from [Hadley Wickham's Tweet on Indexing lists in R](https://twitter.com/hadleywickham/status/643381054758363136?lang=en).
]

---

A matrix is a **2D data structure** made of **one/homogeneous data type.**

.pull-left[

```r
x_mat = matrix( sample(1:10, size = 4), nrow = 2, ncol = 2 ) 
str(x_mat) # its structure?
```

```
##  int [1:2, 1:2] 2 3 10 6
```

```r
x_mat # printing it nicely
print('-----------------')
*x_mat[1, 2] # subsetting
```

```
##      [,1] [,2]
## [1,]    2   10
## [2,]    3    6
## [1] "-----------------"
## [1] 10
```

]

.pull-right[

```r
x_char = matrix(
  sample(letters, size = 12), nrow = 3, ncol =4)
x_char
```

```
##      [,1] [,2] [,3] [,4]
## [1,] "t"  "r"  "l"  "q" 
## [2,] "f"  "v"  "o"  "a" 
## [3,] "u"  "n"  "h"  "d"
```

```r
*x_char[1:2, 2:3] # subsetting
```

```
##      [,1] [,2]
## [1,] "r"  "l" 
## [2,] "v"  "o"
```
]

---

.center[<img src="https://d33wubrfki0l68.cloudfront.net/9ec5e1f8982238a413847eb5c9bbc5dcf44c9893/bc590/diagrams/vectors/summary-tree-s3-2.png" width="22%">]

> .font150[If you do data analysis in R, you’re going to be using data frames. A data frame is a named list of vectors with attributes for `(column)` `names`, `row.names`, and its class, “data.frame”.  -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)]

.footnote[
<html>
<hr>
</html>
**Source:** Image is from [Hadley Wickham's Advanced R: Chapter 3 on Vectors](https://adv-r.hadley.nz/vectors-chap.html#list-array)
]

---

```r
df1 <- data.frame(x = 1:3, y = letters[1:3])
typeof(df1) # showing that its a special case of a list
```

```
## [1] "list"
```

```r
attributes(df1) # but also is of class data.frame
```

```
## $names
## [1] "x" "y"
## 
## $class
## [1] "data.frame"
## 
## $row.names
## [1] 1 2 3
```

In contrast to a regular list, a data frame has **an additional constraint: the length of each of its vectors must be the same.** This gives data frames their **rectangular structure.**

---

As noted in the creation of `df1`, columns in a data frame can be of different types. Hence, it is more widely used in data analysis than matrices.

.center[<img src="../../figures/legos-jbryan-dataframe-w-text.png" width="40%">]

---

> Tibble is a **modern reimagining of the data frame**. Tibbles are designed to be (as much as possible) **drop-in replacements for data frames** that fix those frustrations. A concise, and fun, way to summarise the main differences is that tibbles are **lazy and surly: they do less and complain more**. -- [Hadley Wickham](https://adv-r.hadley.nz/vectors-chap.html#list-array)

.pull-left[[<img src="https://d33wubrfki0l68.cloudfront.net/565916198b0be51bf88b36f94b80c7ea67cafe7c/7f70b/cover.png" height="320px">](https://adv-r.hadley.nz)]

To learn more about the basics of tibble, please consult the reference below: 
* [Data frames and tibbles (Click and read from 3.6 up to and including 3.6.5)](https://adv-r.hadley.nz/vectors-chap.html#list-array)

---

# Translating <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Data Structures to <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg>

<html>
<center>
<table border="1" style="font-size: 0.8em;">
  <tr>
    <th>R Data Structure</th>
    <th>Description</th>
    <th>R Subsetting<br>(Multiple Methods)</th>
    <th>Python Equivalent</th>
    <th>Python Subsetting<br>(Multiple Methods)</th>
  </tr>
  <tr>
    <td>Vector</td>
    <td>1D array, single type</td>
    <td>vector[index]<br>vector[c(1,2)]<br>vector[-1]</td>
    <td>List (with single type)<br>or NumPy array</td>
    <td>list[-1]<br>list[1:3]<br>array[1:3]</td>
  </tr>
  <tr>
    <td>Matrix</td>
    <td>2D array, single type</td>
    <td>matrix[row, col]<br>matrix[1,]<br>matrix[,1]</td>
    <td>2D List (with single type)<br>or 2D NumPy array</td>
    <td>list[row][col]<br>array[row, col]<br>array[row,:]<br>array[:,col]</td>
  </tr>
  <tr>
    <td>Data Frame</td>
    <td>2D table,<br>multiple types</td>
    <td>df[row, col]<br>df[1,]<br>df[, "col"]<br>df$col</td>
    <td>Pandas DataFrame</td>
    <td>df.loc[row, col]<br>df.iloc[row, col]</td>
  </tr>
  <tr>
    <td>List</td>
    <td>Ordered collection,<br>multiple types</td>
    <td>list[[index]]<br>list$element_name<br>list[[1]][1]</td>
    <td>List</td>
    <td>list[index]<br>list[index][subindex]</td>
  </tr>
  <tr>
    <td>Dictionary</td>
    <td>Key-value pairs</td>
    <td>list$element_name</td>
    <td>Dictionary</td>
    <td>dict[key]<br>dict.get(key)</td>
  </tr>
</table>

</center>
</html>

---

A function call consists of the **function name** followed by one or more **argument** within parentheses.

```r
temp_high_forecast = c(86, 84, 85, 89, 89, 84, 81)
mean(x = temp_high_forecast)
```

```
## [1] 85.42857
```

* function name: `mean()`, a built-in R function to compute mean of a vector
* argument: the first argument (LHS `x`) to specify the data (RHS `temp_high_forecast`)

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#41)
]

---

Check the function's help page with `?mean`

### Class Activity

> _Please take 2 minutes to investigate the help page for `mean` in R Studio._

```r
mean(x = temp_high_forecast, trim = 0, na.rm = FALSE, ...)
```

* Read **Usage** section
  + What arguments have default values?
* Read **Arguments** section
  + What does `trim` do?
* Run **Example** code

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#42)
]

---

.pull-left[
### Match by **positions**

```r
mean(temp_high_forecast, 0.1, TRUE)
```

```
## [1] 85.42857
```
]

.pull-right[
### Match by **names**

```r
mean(x = temp_high_forecast, trim = 0.1, na.rm = TRUE)
```

```
## [1] 85.42857
```
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#43)
]

---

# Use Functions from Packages

.pull-left[

```r
library(dplyr)
cummean(temp_high_forecast)
```

```
## [1] 86.00000 85.00000 85.00000 86.00000 86.60000 86.16667 85.42857
```

```r
first(temp_high_forecast)
```

```
## [1] 86
```

```r
last(temp_high_forecast)
```

```
## [1] 81
```
]
.pull-right[
<br>
<br>
<br>
<br>
.center[
<img src="https://hbctraining.github.io/Intro-to-R-flipped/img/install_vs_library.jpeg" height="240px">
]
]

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#44)
]

---

# Write Your Own <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Functions

```r
# function_name <- function(arguments) {
#   function_body
# }
my_mean <- function(x, na.rm = FALSE) {
  summation <- sum(x, na.rm = na.rm)
  summation / length(x)
}

my_mean(temp_high_forecast)
```

```
## [1] 85.42857
```

.footnote[
<html>
<hr>
</html>

**Source:** Slide is based on [Earo Wang's STAT 220 Slides](https://stats220.earo.me/01-intro.html#45)
]

---

# Write Your Own <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#C3142D;overflow:visible;position:relative;"><path d="M439.8 200.5c-7.7-30.9-22.3-54.2-53.4-54.2h-40.1v47.4c0 36.8-31.2 67.8-66.8 67.8H172.7c-29.2 0-53.4 25-53.4 54.3v101.8c0 29 25.2 46 53.4 54.3 33.8 9.9 66.3 11.7 106.8 0 26.9-7.8 53.4-23.5 53.4-54.3v-40.7H226.2v-13.6h160.2c31.1 0 42.6-21.7 53.4-54.2 11.2-33.5 10.7-65.7 0-108.6zM286.2 404c11.1 0 20.1 9.1 20.1 20.3 0 11.3-9 20.4-20.1 20.4-11 0-20.1-9.2-20.1-20.4.1-11.3 9.1-20.3 20.1-20.3zM167.8 248.1h106.8c29.7 0 53.4-24.5 53.4-54.3V91.9c0-29-24.4-50.7-53.4-55.6-35.8-5.9-74.7-5.6-106.8.1-45.2 8-53.4 24.7-53.4 55.6v40.7h106.9v13.6h-147c-31.1 0-58.3 18.7-66.8 54.2-9.8 40.7-10.2 66.1 0 108.6 7.6 31.6 25.7 54.2 56.8 54.2H101v-48.8c0-35.3 30.5-66.4 66.8-66.4zm-6.7-142.6c-11.1 0-20.1-9.1-20.1-20.3.1-11.3 9-20.4 20.1-20.4 11 0 20.1 9.2 20.1 20.4s-9 20.3-20.1 20.3z"/></svg> Functions

```python
# Translating the R function to Python
def my_mean(x, na_rm=False):
    """
    Calculate the mean of a list, with an option to ignore NaN values.

Parameters:
    x (list): List of numbers
    na_rm (bool): Whether to remove NaN values before calculating the mean

Returns:
    float: mean of the list
    """
    if na_rm:
        x = [i for i in x if i is not None]
        
    summation = sum(x)
    return summation / len(x)

# Test the function with a list containing None values
temp_high_forecast = [86, 84, 85, None, 89, 84, 81]
my_mean(temp_high_forecast, na_rm=True)
```

```
## 84.83333333333333
```

---
class: inverse, center, middle

# Generative AI: Large Language Models

---

# Background: Artificial Intelligence

.pull-left[
center[.bold[A [working definition](https://www.brookings.edu/articles/what-is-artificial-intelligence/) for AI]]

.content-box-gray[
.bold[.red[Artificial Intelligence (AI):]] .bold[A system that acts in a way, where people might denote as "intelligent" if another human were to do something similar.]
]

.pull-right[
<img src="data:image/png;base64,#../../figures/ai_applications.png" width="95%" style="display: block; margin: auto;" />
]

.footnote[
<html>
<hr>
</html>

**Image Source:** The flowchart's content and its LaTex code were generated using ChatGPT (May 24 Version).
]

---

# Background: The Road to Generative AI

<br>

```r
knitr::include_graphics('../../figures/generative_ai_chart.png')
```

.footnote[
<html>
<hr>
</html>

**Comment:** You have been hearing about **big data** in SPC for over a decade now. We now have models that can digest and generate answers based on more than 45TB of text. 
]

---

# Background: Generative AI

.content-box-gray[
.bold[.red[Generative AI:]] .bold[The objective is to generate new content rather than analyze existing data.]
]

.font90[
-  The generated content is based on a .bold[.red[stochastic behavior embedded in generative AI models such that the same input prompts results in different content]].
- State-of-the-art generative AI models can have up to **540 billion parameters** ([PaLM](https://arxiv.org/abs/2204.02311)).  
- With the increase in model size, researchers have observed the **“emergent abilities”** of LLMs, which were **not explicitly encoded in the training**. [Examples include](https://ai.googleblog.com/2022/11/characterizing-emergent-phenomena-in.html):
  + Multi-step arithmetic,   
  + taking college-level exams, and 
  + identifying the intended meaning of a word.  
- LLMs are **foundation models** (see [Bommasani et al. 2021](https://arxiv.org/abs/2108.07258)), large pre-trained AI systems
that can be **repurposed with minimal effort across numerous domains and diverse tasks.**
]

---

# LLMs: Natural Language Based Coding

Let us break down this prompt with ChatGPT

.font100[
 > I want you to help me use R to create an animated plot of the unemployment rate by state from FRED.  Here are the steps that I want you to follow:

> 1. Pull the unemployment rate for all 48 states from "2003-01-01" until "2023-01-08". The symbol will be the two letter state abbreviation + UR (e.g., OHUR).  
  
  > 2. Add a column that contains the state name.  
  
  > 3. Use a choropleth map, where the unemployment rates are plotting using a sequential color scheme that is colorblind friendly (use RColorBrewer).  
  
  > 4. Create the animation, where each frame corresponds to a time interval. The animation should have 12 fps and show the date as part of the title. 
  
  > 5. Save the animation as a GIF. 
]

---
class: inverse, center, middle

# Recap

---

# Summary of Main Points

By now, you should be able to do the following:

- Describe how and why we use scripted languages in this course.

- Understand the potential impact of LLMs on businesses and explore how they can be leveraged in the context of this class.

---
---

# 📝 Review and Clarification 📝

1. **Class Notes**: Take some time to revisit your class notes for key insights and concepts.
2. **Zoom Recording**: The recording of today's class will be made available on Canvas approximately 3-4 hours after the session ends.
3. **Questions**: Please don't hesitate to ask for clarification on any topics discussed in class. It's crucial not to let questions accumulate.

---

# 📖 Required Readings 📖

#### 🤖 LLM: Prep

- [AI and the Future of Work in Statistical Quality Control: ChatSQC](https://arxiv.org/pdf/2308.13550.pdf).   
  + Read the **abstract**, **Sections 1, 4, and 5**; feel free to skim sections 2-3.   
  + Please feel free to test the app at: <https://chatsqc.fsb.miamioh.edu/>.

---

# 🎯 Assignment 🎯

- Complete [Assignment 02](https://miamioh.instructure.com/courses/202961/quizzes/582076/) on Canvas to reinforce your understanding and application of the topics covered today as well as the assigned readings.